import os
import sys
import tarfile
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.core.framework import graph_pb2
from tensorflow.python.platform import gfile

from google.protobuf import text_format


sys.path.append("..")


 
# Path to frozen detection graph
CWD_PATH = os.getcwd()
 
PATH_TO_CKPT = os.path.join(CWD_PATH,'frozen_inference_graph_m.pb')

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
 
# List of the strings that is used to add correct label for each box.
 
PATH_TO_LABELS = os.path.join(CWD_PATH,'majiang.txt')


with ops.Graph().as_default():
    output_graph_def = graph_pb2.GraphDef()
    with gfile.FastGFile(PATH_TO_CKPT, 'rb') as f:
        proto_b=f.read()
        output_graph_def.ParseFromString(proto_b)


NUM_CLASSES = 28
detection_graph = tf.Graph()
with ops.Graph().as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with gfile.FastGFile(PATH_TO_CKPT,'rb') as fid:
        od_graph_def.ParseFromString(fid.read())
        tf.import_graph_def(od_graph_def, name='')
        label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
        categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
        category_index = label_map_util.create_category_index(categories)
 

def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)
 
 
 
with detection_graph.as_default():
 
    with tf.Session(graph=detection_graph) as sess:
        image_np = cv2.imread("test.jpg")
        cv2.imshow("input", image_np)
        print(image_np.shape)
        # image_np == [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        scores = detection_graph.get_tensor_by_name('detection_scores:0')
        classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        # Actual detection.
        (boxes, scores, classes, num_detections) = sess.run(
            [boxes, scores, classes, num_detections],
            feed_dict={image_tensor: image_np_expanded})
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
              image_np,
              np.squeeze(boxes),
              np.squeeze(classes).astype(np.int32),
              np.squeeze(scores),
              category_index,
              use_normalized_coordinates=True,
              min_score_thresh=0.4,
              line_thickness=3)
        cv2.imshow('object detection', image_np)
        cv2.imwrite("run_result.png", image_np)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
sess.close()